*This report was originally published in May 2024. It was updated in April 2025 to reflect regulatory changes and advancements in AI technology.

This guide is designed to equip CISOs and senior security executives with a practical framework for building your AI security program.

We provide a comprehensive, actionable roadmap for building your AI security program that is grounded in decades of security expertise and applied AI research and development. Topics covered include:

Secure AI: A CISO's roadmap for risk-ready innovation

Artificial Intelligence is rapidly reshaping business and cybersecurity. As GenAI becomes embedded across enterprise functions, CISOs are tasked with leading not only the protection of AI but also its responsible enablement. Without secure AI practices in place, organizations face significant regulatory, reputational and operational risks.

Why AI security is now a business priority

The rise of shadow AI, deepfakes, data leakage and adversarial threats means traditional security practices no longer suffice. AI is a powerful tool—but also a new attack surface. Security must be embedded in AI systems from development through deployment. CISOs must move fast to protect sensitive data, align with new regulations and adapt to evolving threat landscapes.

Strategic foundations for a secure AI program

1. Full-Spectrum AI Security
CISOs must secure:

Internal AI models and GenAI tools

Third-party copilots and agents

Browser and mobile-based AI apps

SaaS-integrated AI services

Key techniques include zero trust, IAM, data loss prevention, content filtering and secure service edge architectures.

2. Governance-Driven Protection
A successful secure AI strategy includes:

Building an AI Center of Excellence (AICoE)

Defining clear roles across business units

Aligning AI security with data governance and vulnerability management

Conducting regular red and blue team simulations

3. Combatting Modern Threats
Security teams must anticipate:

Prompt injection and model poisoning

Sensitive data leakage and improper output handling

AI-powered phishing and voice-based biometric spoofing

Shadow AI growth across departments

Explore WWT's AI Cyber Range capabilities

AI as a force multiplier for security

Secure AI isn't just a defense mechanism. It's a business enabler. With the right implementation, AI security can:

Reduce risk exposure across data and workflows

Drive operational efficiency through automation

Accelerate secure revenue growth via faster product cycles and improved customer trust

Use cases include threat detection, anomaly tracking, fraud detection, automated policy generation, and incident summarization. AI also enhances identity governance with behavior-based access controls and proactive alerting.

Navigating compliance and regulation

CISOs must stay ahead of global standards such as:

EU AI Act: Risk-based categorization of AI systems with steep penalties for noncompliance

NIST AI RMF & Generative AI Profile: Guidelines to govern and manage GenAI risks

OWASP LLM Top 10: Frameworks for mitigating AI-specific vulnerabilities

State-level AI laws in the U.S.: Fragmented but fast-growing

Staying compliant means embedding governance into each stage of AI development and use, from model training to prompt engineering and access control.

Building a scalable security program for AI

WWT recommends a four-phase approach:

Discovery & Gap Analysis – Assess AI use and existing defenses

Governance & Policy Design – Create a unified AI security framework

Proof of Concept & Testing – Evaluate secure integration paths

Implementation & Monitoring – Execute and measure performance continuously

With the AI Proving Ground, organizations can test security solutions in a lab before deployment.

Secure AI frameworks worth watching

SAIF (Google) – Secure AI Framework

MITRE ATLAS – AI adversarial defense mapping

ISO/IEC 42001 – AI governance standards

LLM firewalls, gateways and proxies – Emerging product categories

Integrating these frameworks into your enterprise stack enables visibility, threat mitigation and scalable controls.

Top questions about AI security

These are the most searched and discussed questions by security leaders and decision-makers exploring CISO AI, secure AI and AI security strategies:

Technical & strategy

What is the best framework for securing GenAI?

How can CISOs control shadow AI in their organization?

What tools help mitigate AI-specific threats like prompt injection or model theft?

Should I build custom LLMs or secure third-party GenAI tools?

Risk & compliance

How does the EU AI Act affect AI security requirements?

What's the difference between NIST's AI RMF and OWASP's Top 10 for LLMs?

How can I monitor third-party AI tool usage to reduce compliance risk?

What are the top risks to watch for in GenAI security audits?

Deployment & operations

What is a secure-by-design approach to AI deployment?

How do I secure AI chatbots and copilots in enterprise environments?

How should security teams test AI models for vulnerability?

AI for security

How is AI used to detect threats faster than human analysts?

Can AI be used to write and enforce security policies?

What are the top GenAI tools for incident response?

Conclusion

AI adoption is inevitable. Securing AI is not just a compliance checkbox—it's a core business differentiator. For CISOs, building a robust, scalable and future-ready AI security program means aligning governance, tools, talent and infrastructure to meet today's threats and tomorrow's opportunities. Now is the time to act.

Emily Velders

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